Overview

Dataset statistics

Number of variables10
Number of observations7143
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory220.0 B

Variable types

Numeric8
Categorical2

Alerts

Name has a high cardinality: 7079 distinct values High cardinality
BGGId is highly correlated with YearPublished and 1 other fieldsHigh correlation
YearPublished is highly correlated with BGGId and 1 other fieldsHigh correlation
MfgPlaytime is highly correlated with MfgAgeRecHigh correlation
AvgRating is highly correlated with BGGId and 1 other fieldsHigh correlation
MfgAgeRec is highly correlated with MfgPlaytimeHigh correlation
BGGId is highly correlated with AvgRatingHigh correlation
MfgPlaytime is highly correlated with MfgAgeRecHigh correlation
AvgRating is highly correlated with BGGIdHigh correlation
MfgAgeRec is highly correlated with MfgPlaytimeHigh correlation
BGGId is highly correlated with YearPublishedHigh correlation
YearPublished is highly correlated with BGGIdHigh correlation
BGGId is highly correlated with AvgRatingHigh correlation
Category is highly correlated with MfgPlaytime and 2 other fieldsHigh correlation
MfgPlaytime is highly correlated with Category and 1 other fieldsHigh correlation
MinPlayers is highly correlated with MaxPlayersHigh correlation
MaxPlayers is highly correlated with Category and 1 other fieldsHigh correlation
AvgRating is highly correlated with BGGIdHigh correlation
MfgAgeRec is highly correlated with Category and 1 other fieldsHigh correlation
YearPublished is highly skewed (γ1 = -27.68411069) Skewed
Name is uniformly distributed Uniform
BGGId has unique values Unique

Reproduction

Analysis started2022-02-17 19:04:08.869143
Analysis finished2022-02-17 19:04:18.876354
Duration10.01 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

BGGId
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct7143
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83656.89626
Minimum2
Maximum346703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.6 KiB
2022-02-17T13:04:18.958864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile840.7
Q16926.5
median34373
Q3155847
95-th percentile264237.1
Maximum346703
Range346701
Interquartile range (IQR)148920.5

Descriptive statistics

Standard deviation91790.18935
Coefficient of variation (CV)1.097222028
Kurtosis-0.5634870771
Mean83656.89626
Median Absolute Deviation (MAD)32674
Skewness0.871992766
Sum597561210
Variance8425438861
MonotonicityStrictly increasing
2022-02-17T13:04:19.027819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21
 
< 0.1%
311
 
< 0.1%
71
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
161
 
< 0.1%
171
 
< 0.1%
191
 
< 0.1%
Other values (7133)7133
99.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
161
< 0.1%
171
< 0.1%
ValueCountFrequency (%)
3467031
< 0.1%
3435621
< 0.1%
3429421
< 0.1%
3392141
< 0.1%
3386281
< 0.1%
3377871
< 0.1%
3356091
< 0.1%
3352751
< 0.1%
3329441
< 0.1%
3301451
< 0.1%

Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct7079
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
Quantum
 
3
Samurai
 
3
Around the World in 80 Days
 
3
Crossfire
 
2
Richelieu
 
2
Other values (7074)
7130 

Length

Max length107
Median length14
Mean length18.11591768
Min length1

Characters and Unicode

Total characters129402
Distinct characters129
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7018 ?
Unique (%)98.3%

Sample

1st rowDragonmaster
2nd rowSamurai
3rd rowAcquire
4th rowCathedral
5th rowEl Caballero

Common Values

ValueCountFrequency (%)
Quantum3
 
< 0.1%
Samurai3
 
< 0.1%
Around the World in 80 Days3
 
< 0.1%
Crossfire2
 
< 0.1%
Richelieu2
 
< 0.1%
Buffy the Vampire Slayer: The Board Game2
 
< 0.1%
Touché2
 
< 0.1%
Equinox2
 
< 0.1%
Coup2
 
< 0.1%
Hellas2
 
< 0.1%
Other values (7069)7120
99.7%

Length

2022-02-17T13:04:19.128163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1477
 
7.0%
of832
 
3.9%
game409
 
1.9%
war231
 
1.1%
200
 
0.9%
in196
 
0.9%
edition150
 
0.7%
battle142
 
0.7%
a130
 
0.6%
card122
 
0.6%
Other values (7344)17301
81.6%

Most occurring characters

ValueCountFrequency (%)
14071
 
10.9%
e11426
 
8.8%
a9702
 
7.5%
o7514
 
5.8%
r7434
 
5.7%
i6821
 
5.3%
n6590
 
5.1%
t6546
 
5.1%
s5336
 
4.1%
l4499
 
3.5%
Other values (119)49463
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter90420
69.9%
Uppercase Letter18787
 
14.5%
Space Separator14071
 
10.9%
Other Punctuation2952
 
2.3%
Decimal Number2538
 
2.0%
Dash Punctuation432
 
0.3%
Open Punctuation92
 
0.1%
Close Punctuation92
 
0.1%
Math Symbol5
 
< 0.1%
Currency Symbol5
 
< 0.1%
Other values (4)8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e11426
12.6%
a9702
10.7%
o7514
 
8.3%
r7434
 
8.2%
i6821
 
7.5%
n6590
 
7.3%
t6546
 
7.2%
s5336
 
5.9%
l4499
 
5.0%
h3296
 
3.6%
Other values (43)21256
23.5%
Uppercase Letter
ValueCountFrequency (%)
T1853
 
9.9%
C1639
 
8.7%
S1621
 
8.6%
B1217
 
6.5%
A1168
 
6.2%
D1088
 
5.8%
G1080
 
5.7%
M1073
 
5.7%
W971
 
5.2%
R869
 
4.6%
Other values (25)6208
33.0%
Other Punctuation
ValueCountFrequency (%)
:1612
54.6%
!353
 
12.0%
'342
 
11.6%
,224
 
7.6%
&186
 
6.3%
.161
 
5.5%
?42
 
1.4%
/15
 
0.5%
#6
 
0.2%
"4
 
0.1%
Other values (5)7
 
0.2%
Decimal Number
ValueCountFrequency (%)
1676
26.6%
4309
12.2%
0303
11.9%
9301
11.9%
2190
 
7.5%
8179
 
7.1%
5178
 
7.0%
6142
 
5.6%
3139
 
5.5%
7121
 
4.8%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Dash Punctuation
ValueCountFrequency (%)
-337
78.0%
95
 
22.0%
Open Punctuation
ValueCountFrequency (%)
(91
98.9%
[1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
)91
98.9%
]1
 
1.1%
Space Separator
ValueCountFrequency (%)
14071
100.0%
Math Symbol
ValueCountFrequency (%)
+5
100.0%
Currency Symbol
ValueCountFrequency (%)
$5
100.0%
Other Number
ValueCountFrequency (%)
2
100.0%
Nonspacing Mark
ValueCountFrequency (%)
́1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin109207
84.4%
Common20190
 
15.6%
Han4
 
< 0.1%
Inherited1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e11426
 
10.5%
a9702
 
8.9%
o7514
 
6.9%
r7434
 
6.8%
i6821
 
6.2%
n6590
 
6.0%
t6546
 
6.0%
s5336
 
4.9%
l4499
 
4.1%
h3296
 
3.0%
Other values (78)40043
36.7%
Common
ValueCountFrequency (%)
14071
69.7%
:1612
 
8.0%
1676
 
3.3%
!353
 
1.7%
'342
 
1.7%
-337
 
1.7%
4309
 
1.5%
0303
 
1.5%
9301
 
1.5%
,224
 
1.1%
Other values (26)1662
 
8.2%
Han
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Inherited
ValueCountFrequency (%)
́1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII129098
99.8%
None203
 
0.2%
Punctuation96
 
0.1%
CJK4
 
< 0.1%
Diacriticals1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14071
 
10.9%
e11426
 
8.9%
a9702
 
7.5%
o7514
 
5.8%
r7434
 
5.8%
i6821
 
5.3%
n6590
 
5.1%
t6546
 
5.1%
s5336
 
4.1%
l4499
 
3.5%
Other values (72)49159
38.1%
Punctuation
ValueCountFrequency (%)
95
99.0%
1
 
1.0%
None
ValueCountFrequency (%)
é36
17.7%
ü34
16.7%
ä33
16.3%
ö19
9.4%
ó8
 
3.9%
ñ8
 
3.9%
í7
 
3.4%
à6
 
3.0%
á5
 
2.5%
ß4
 
2.0%
Other values (30)43
21.2%
Diacriticals
ValueCountFrequency (%)
́1
100.0%
CJK
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

YearPublished
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct145
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.236175
Minimum-3500
Maximum2021
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)0.1%
Memory size118.6 KiB
2022-02-17T13:04:19.207017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3500
5-th percentile1974
Q11997
median2008
Q32014
95-th percentile2019
Maximum2021
Range5521
Interquartile range (IQR)17

Descriptive statistics

Standard deviation143.2202348
Coefficient of variation (CV)0.07174513544
Kurtosis872.0692842
Mean1996.236175
Median Absolute Deviation (MAD)8
Skewness-27.68411069
Sum14259115
Variance20512.03564
MonotonicityNot monotonic
2022-02-17T13:04:19.275526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015339
 
4.7%
2017306
 
4.3%
2016296
 
4.1%
2013292
 
4.1%
2010290
 
4.1%
2012289
 
4.0%
2018282
 
3.9%
2009282
 
3.9%
2014282
 
3.9%
2019267
 
3.7%
Other values (135)4218
59.1%
ValueCountFrequency (%)
-35001
< 0.1%
-30001
< 0.1%
-26001
< 0.1%
-22001
< 0.1%
-14002
< 0.1%
-2001
< 0.1%
-1001
< 0.1%
4001
< 0.1%
5502
< 0.1%
7002
< 0.1%
ValueCountFrequency (%)
202177
 
1.1%
2020162
2.3%
2019267
3.7%
2018282
3.9%
2017306
4.3%
2016296
4.1%
2015339
4.7%
2014282
3.9%
2013292
4.1%
2012289
4.0%

Category
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size497.0 KiB
War
1634 
Strategy
1429 
Family
1408 
Abstract
776 
Childrens
692 
Other values (3)
1204 

Length

Max length9
Median length6
Mean length6.253114938
Min length3

Characters and Unicode

Total characters44666
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStrategy
2nd rowStrategy
3rd rowStrategy
4th rowAbstract
5th rowStrategy

Common Values

ValueCountFrequency (%)
War1634
22.9%
Strategy1429
20.0%
Family1408
19.7%
Abstract776
10.9%
Childrens692
9.7%
Thematic616
 
8.6%
Party378
 
5.3%
CGS210
 
2.9%

Length

2022-02-17T13:04:19.371509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-17T13:04:19.430829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
war1634
22.9%
strategy1429
20.0%
family1408
19.7%
abstract776
10.9%
childrens692
9.7%
thematic616
 
8.6%
party378
 
5.3%
cgs210
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a6241
14.0%
t5404
12.1%
r4909
11.0%
y3215
 
7.2%
e2737
 
6.1%
i2716
 
6.1%
l2100
 
4.7%
m2024
 
4.5%
S1639
 
3.7%
W1634
 
3.7%
Other values (13)12047
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37103
83.1%
Uppercase Letter7563
 
16.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6241
16.8%
t5404
14.6%
r4909
13.2%
y3215
8.7%
e2737
7.4%
i2716
7.3%
l2100
 
5.7%
m2024
 
5.5%
s1468
 
4.0%
g1429
 
3.9%
Other values (5)4860
13.1%
Uppercase Letter
ValueCountFrequency (%)
S1639
21.7%
W1634
21.6%
F1408
18.6%
C902
11.9%
A776
10.3%
T616
 
8.1%
P378
 
5.0%
G210
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin44666
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6241
14.0%
t5404
12.1%
r4909
11.0%
y3215
 
7.2%
e2737
 
6.1%
i2716
 
6.1%
l2100
 
4.7%
m2024
 
4.5%
S1639
 
3.7%
W1634
 
3.7%
Other values (13)12047
27.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII44666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a6241
14.0%
t5404
12.1%
r4909
11.0%
y3215
 
7.2%
e2737
 
6.1%
i2716
 
6.1%
l2100
 
4.7%
m2024
 
4.5%
S1639
 
3.7%
W1634
 
3.7%
Other values (13)12047
27.0%

MfgPlaytime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.68038639
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.6 KiB
2022-02-17T13:04:19.508354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q130
median50
Q390
95-th percentile180
Maximum200
Range199
Interquartile range (IQR)60

Descriptive statistics

Standard deviation48.24360225
Coefficient of variation (CV)0.7345206827
Kurtosis0.01787412507
Mean65.68038639
Median Absolute Deviation (MAD)30
Skewness0.9697862874
Sum469155
Variance2327.445159
MonotonicityNot monotonic
2022-02-17T13:04:19.570988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
301183
16.6%
601090
15.3%
120966
13.5%
90720
10.1%
45698
9.8%
20693
9.7%
180518
7.3%
15391
 
5.5%
10296
 
4.1%
40149
 
2.1%
Other values (28)439
 
6.1%
ValueCountFrequency (%)
15
 
0.1%
21
 
< 0.1%
42
 
< 0.1%
536
 
0.5%
71
 
< 0.1%
10296
4.1%
121
 
< 0.1%
131
 
< 0.1%
15391
5.5%
20693
9.7%
ValueCountFrequency (%)
2009
 
0.1%
180518
7.3%
1651
 
< 0.1%
15096
 
1.3%
1405
 
0.1%
1352
 
< 0.1%
1251
 
< 0.1%
120966
13.5%
1152
 
< 0.1%
1102
 
< 0.1%

MinPlayers
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.959400812
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.6 KiB
2022-02-17T13:04:19.645333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.598493656
Coefficient of variation (CV)0.3054472839
Kurtosis7.339589686
Mean1.959400812
Median Absolute Deviation (MAD)0
Skewness1.155103852
Sum13996
Variance0.3581946563
MonotonicityNot monotonic
2022-02-17T13:04:19.808361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
25147
72.1%
11222
 
17.1%
3645
 
9.0%
4112
 
1.6%
511
 
0.2%
83
 
< 0.1%
63
 
< 0.1%
ValueCountFrequency (%)
11222
 
17.1%
25147
72.1%
3645
 
9.0%
4112
 
1.6%
511
 
0.2%
63
 
< 0.1%
83
 
< 0.1%
ValueCountFrequency (%)
83
 
< 0.1%
63
 
< 0.1%
511
 
0.2%
4112
 
1.6%
3645
 
9.0%
25147
72.1%
11222
 
17.1%

MaxPlayers
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.320593588
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.6 KiB
2022-02-17T13:04:19.879366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q35
95-th percentile8
Maximum20
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.274369897
Coefficient of variation (CV)0.526402183
Kurtosis8.926887386
Mean4.320593588
Median Absolute Deviation (MAD)2
Skewness2.057022232
Sum30862
Variance5.172758427
MonotonicityNot monotonic
2022-02-17T13:04:19.948395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
42340
32.8%
21880
26.3%
51032
14.4%
61022
14.3%
8299
 
4.2%
3120
 
1.7%
1115
 
1.6%
10102
 
1.4%
793
 
1.3%
1267
 
0.9%
Other values (8)73
 
1.0%
ValueCountFrequency (%)
1115
 
1.6%
21880
26.3%
3120
 
1.7%
42340
32.8%
51032
14.4%
61022
14.3%
793
 
1.3%
8299
 
4.2%
917
 
0.2%
10102
 
1.4%
ValueCountFrequency (%)
2018
 
0.3%
188
 
0.1%
1615
 
0.2%
158
 
0.1%
142
 
< 0.1%
134
 
0.1%
1267
0.9%
111
 
< 0.1%
10102
1.4%
917
 
0.2%

AvgRating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct489
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.522428951
Minimum2.08
Maximum9.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.6 KiB
2022-02-17T13:04:20.031427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.08
5-th percentile5.04
Q16.01
median6.56
Q37.11
95-th percentile7.81
Maximum9.14
Range7.06
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.8482118967
Coefficient of variation (CV)0.1300454023
Kurtosis0.5259198637
Mean6.522428951
Median Absolute Deviation (MAD)0.55
Skewness-0.4183916488
Sum46589.71
Variance0.7194634217
MonotonicityNot monotonic
2022-02-17T13:04:20.098074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7351
 
0.7%
6.5849
 
0.7%
6.7146
 
0.6%
6.4346
 
0.6%
6.7245
 
0.6%
6.5544
 
0.6%
6.3744
 
0.6%
6.6242
 
0.6%
6.4442
 
0.6%
6.6742
 
0.6%
Other values (479)6692
93.7%
ValueCountFrequency (%)
2.081
< 0.1%
2.791
< 0.1%
2.871
< 0.1%
2.931
< 0.1%
3.111
< 0.1%
3.191
< 0.1%
3.331
< 0.1%
3.341
< 0.1%
3.382
< 0.1%
3.442
< 0.1%
ValueCountFrequency (%)
9.141
< 0.1%
8.921
< 0.1%
8.881
< 0.1%
8.871
< 0.1%
8.851
< 0.1%
8.841
< 0.1%
8.831
< 0.1%
8.811
< 0.1%
8.792
< 0.1%
8.781
< 0.1%

MfgAgeRec
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.25171497
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.6 KiB
2022-02-17T13:04:20.167437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median10
Q312
95-th percentile14
Maximum21
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.737038048
Coefficient of variation (CV)0.2669834322
Kurtosis-0.1664961678
Mean10.25171497
Median Absolute Deviation (MAD)2
Skewness-0.4180382683
Sum73228
Variance7.491377274
MonotonicityNot monotonic
2022-02-17T13:04:20.231928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
122124
29.7%
101423
19.9%
81285
18.0%
14636
 
8.9%
13438
 
6.1%
7277
 
3.9%
6274
 
3.8%
5193
 
2.7%
4157
 
2.2%
9107
 
1.5%
Other values (8)229
 
3.2%
ValueCountFrequency (%)
28
 
0.1%
371
 
1.0%
4157
 
2.2%
5193
 
2.7%
6274
 
3.8%
7277
 
3.9%
81285
18.0%
9107
 
1.5%
101423
19.9%
1132
 
0.4%
ValueCountFrequency (%)
211
 
< 0.1%
1825
 
0.3%
1713
 
0.2%
1635
 
0.5%
1544
 
0.6%
14636
 
8.9%
13438
 
6.1%
122124
29.7%
1132
 
0.4%
101423
19.9%

NumUserRatings
Real number (ℝ≥0)

Distinct2574
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1748.573009
Minimum30
Maximum107937
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.6 KiB
2022-02-17T13:04:20.322978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile37
Q197
median356
Q31232
95-th percentile7809.8
Maximum107937
Range107907
Interquartile range (IQR)1135

Descriptive statistics

Standard deviation5106.253376
Coefficient of variation (CV)2.920240305
Kurtosis92.58596226
Mean1748.573009
Median Absolute Deviation (MAD)304
Skewness8.007539833
Sum12490057
Variance26073823.54
MonotonicityNot monotonic
2022-02-17T13:04:20.422690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3163
 
0.9%
3059
 
0.8%
3450
 
0.7%
4049
 
0.7%
4448
 
0.7%
3248
 
0.7%
4348
 
0.7%
3645
 
0.6%
3343
 
0.6%
3742
 
0.6%
Other values (2564)6648
93.1%
ValueCountFrequency (%)
3059
0.8%
3163
0.9%
3248
0.7%
3343
0.6%
3450
0.7%
3535
0.5%
3645
0.6%
3742
0.6%
3834
0.5%
3939
0.5%
ValueCountFrequency (%)
1079371
< 0.1%
811311
< 0.1%
755311
< 0.1%
735221
< 0.1%
730931
< 0.1%
682941
< 0.1%
658101
< 0.1%
651871
< 0.1%
639861
< 0.1%
637791
< 0.1%

Interactions

2022-02-17T13:04:17.982077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.024864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.766754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.386183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.036794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.782490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.423384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:17.104247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:18.066753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.188259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.847816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.470052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.129579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.854355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.502603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:17.204439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:18.140653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.266107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.922973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.560895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.199882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.938005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.578774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:17.280435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:18.227806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.364034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.003436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.644912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.279674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.020469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.640502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:17.377384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:18.306077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.437798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.087608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.726144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.474245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.101592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.742415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:17.480846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:18.389528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.531061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.153262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.801889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.538100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.185357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.850506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:17.573845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:18.469082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.613389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.236287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.894164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.629826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.264905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.958934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:17.667629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:18.549684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:13.685651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.313760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:14.976957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:15.702635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:16.344667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:17.041346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-17T13:04:17.892240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-17T13:04:20.489111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-17T13:04:20.579896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-17T13:04:20.678156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-17T13:04:20.793774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-17T13:04:18.662535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-17T13:04:18.799100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

BGGIdNameYearPublishedCategoryMfgPlaytimeMinPlayersMaxPlayersAvgRatingMfgAgeRecNumUserRatings
02Dragonmaster1981Strategy30346.6512562
13Samurai1998Strategy60247.461015146
25Acquire1964Strategy90267.341218655
37Cathedral1978Abstract20226.5283320
49El Caballero1998Strategy90246.45131389
510Elfenland1998Family60266.7108324
611Bohnanza1997Family45277.041339886
712Ra1999Strategy60257.481219685
816MarraCash1996Strategy60346.8312964
917Button Men1999CGS5226.3710804

Last rows

BGGIdNameYearPublishedCategoryMfgPlaytimeMinPlayersMaxPlayersAvgRatingMfgAgeRecNumUserRatings
7133330145La Guerra de la Triple Alianza2021War150227.691438
7134332944Sobek: 2 Players2021Family20227.2510300
7135335275Whirling Witchcraft2021Family30257.2814312
7136335609TEN2021Family30157.1510451
7137337787Summer Camp2021Family45247.4210445
7138338628TRAILS2021Family40247.2910554
7139339214HIT !2021Family20257.33831
7140342942Ark Nova2021Strategy150148.4814618
7141343562Horrified: American Monsters2021Strategy60157.8710334
71423467037 Wonders: Architects2021Family25277.228949